Overview

Dataset statistics

Number of variables13
Number of observations1224
Missing cells841
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory124.4 KiB
Average record size in memory104.1 B

Variable types

Numeric9
Text2
Categorical1
DateTime1

Alerts

popularity is highly overall correlated with sellers_amountHigh correlation
best_price is highly overall correlated with lowest_price and 3 other fieldsHigh correlation
lowest_price is highly overall correlated with best_price and 3 other fieldsHigh correlation
highest_price is highly overall correlated with best_price and 3 other fieldsHigh correlation
sellers_amount is highly overall correlated with popularityHigh correlation
screen_size is highly overall correlated with best_price and 5 other fieldsHigh correlation
memory_size is highly overall correlated with best_price and 3 other fieldsHigh correlation
battery_size is highly overall correlated with screen_sizeHigh correlation
os is highly overall correlated with screen_sizeHigh correlation
os is highly imbalanced (78.4%)Imbalance
os has 197 (16.1%) missing valuesMissing
lowest_price has 260 (21.2%) missing valuesMissing
highest_price has 260 (21.2%) missing valuesMissing
memory_size has 112 (9.2%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
popularity is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
popularity has unique valuesUnique

Reproduction

Analysis started2023-10-22 10:43:57.248418
Analysis finished2023-10-22 10:44:10.526957
Duration13.28 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1224
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean611.5
Minimum0
Maximum1223
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-10-22T16:14:10.642481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61.15
Q1305.75
median611.5
Q3917.25
95-th percentile1161.85
Maximum1223
Range1223
Interquartile range (IQR)611.5

Descriptive statistics

Standard deviation353.48267
Coefficient of variation (CV)0.57805834
Kurtosis-1.2
Mean611.5
Median Absolute Deviation (MAD)306
Skewness0
Sum748476
Variance124950
MonotonicityStrictly increasing
2023-10-22T16:14:10.939490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
813 1
 
0.1%
820 1
 
0.1%
819 1
 
0.1%
818 1
 
0.1%
817 1
 
0.1%
816 1
 
0.1%
815 1
 
0.1%
814 1
 
0.1%
812 1
 
0.1%
Other values (1214) 1214
99.2%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
1223 1
0.1%
1222 1
0.1%
1221 1
0.1%
1220 1
0.1%
1219 1
0.1%
1218 1
0.1%
1217 1
0.1%
1216 1
0.1%
1215 1
0.1%
1214 1
0.1%
Distinct64
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size9.7 KiB
2023-10-22T16:14:11.130645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length12
Median length9
Mean length6.0580065
Min length2

Characters and Unicode

Total characters7415
Distinct characters51
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)1.0%

Sample

1st rowALCATEL
2nd rowALCATEL
3rd rowALCATEL
4th rowALCATEL
5th rowNokia
ValueCountFrequency (%)
samsung 168
 
13.2%
xiaomi 111
 
8.7%
apple 102
 
8.0%
motorola 62
 
4.9%
sigma 52
 
4.1%
mobile 52
 
4.1%
huawei 49
 
3.8%
nokia 48
 
3.8%
doogee 44
 
3.4%
blackview 42
 
3.3%
Other values (55) 546
42.8%
2023-10-22T16:14:11.481146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 714
 
9.6%
a 552
 
7.4%
i 547
 
7.4%
e 481
 
6.5%
m 457
 
6.2%
l 408
 
5.5%
n 331
 
4.5%
S 271
 
3.7%
u 249
 
3.4%
g 241
 
3.3%
Other values (41) 3164
42.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5256
70.9%
Uppercase Letter 2088
 
28.2%
Space Separator 52
 
0.7%
Decimal Number 14
 
0.2%
Dash Punctuation 5
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 714
13.6%
a 552
10.5%
i 547
10.4%
e 481
9.2%
m 457
8.7%
l 408
 
7.8%
n 331
 
6.3%
u 249
 
4.7%
g 241
 
4.6%
s 234
 
4.5%
Other values (14) 1042
19.8%
Uppercase Letter
ValueCountFrequency (%)
S 271
13.0%
A 236
11.3%
E 220
 
10.5%
O 190
 
9.1%
G 125
 
6.0%
M 118
 
5.7%
X 111
 
5.3%
U 99
 
4.7%
H 85
 
4.1%
N 79
 
3.8%
Other values (14) 554
26.5%
Space Separator
ValueCountFrequency (%)
52
100.0%
Decimal Number
ValueCountFrequency (%)
2 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7344
99.0%
Common 71
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 714
 
9.7%
a 552
 
7.5%
i 547
 
7.4%
e 481
 
6.5%
m 457
 
6.2%
l 408
 
5.6%
n 331
 
4.5%
S 271
 
3.7%
u 249
 
3.4%
g 241
 
3.3%
Other values (38) 3093
42.1%
Common
ValueCountFrequency (%)
52
73.2%
2 14
 
19.7%
- 5
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7415
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 714
 
9.6%
a 552
 
7.4%
i 547
 
7.4%
e 481
 
6.5%
m 457
 
6.2%
l 408
 
5.5%
n 331
 
4.5%
S 271
 
3.7%
u 249
 
3.4%
g 241
 
3.3%
Other values (41) 3164
42.7%
Distinct1068
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Memory size9.7 KiB
2023-10-22T16:14:11.782705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length67
Median length48
Mean length27.07598
Min length3

Characters and Unicode

Total characters33141
Distinct characters70
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique930 ?
Unique (%)76.0%

Sample

1st row1 1/8GB Bluish Black (5033D-2JALUAA)
2nd row1 5033D 1/16GB Volcano Black (5033D-2LALUAF)
3rd row1 5033D 1/16GB Volcano Black (5033D-2LALUAF)
4th row1 5033D 1/16GB Volcano Black (5033D-2LALUAF)
5th row1.3 1/16GB Charcoal
ValueCountFrequency (%)
black 589
 
10.3%
galaxy 167
 
2.9%
4/64gb 163
 
2.8%
blue 156
 
2.7%
pro 125
 
2.2%
iphone 102
 
1.8%
dual 88
 
1.5%
redmi 87
 
1.5%
6/128gb 84
 
1.5%
sim 78
 
1.4%
Other values (1149) 4098
71.4%
2023-10-22T16:14:12.471775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4513
 
13.6%
B 1832
 
5.5%
G 1599
 
4.8%
a 1505
 
4.5%
l 1431
 
4.3%
2 1232
 
3.7%
e 1230
 
3.7%
1 1140
 
3.4%
0 926
 
2.8%
6 858
 
2.6%
Other values (60) 16875
50.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11114
33.5%
Uppercase Letter 8151
24.6%
Decimal Number 7466
22.5%
Space Separator 4513
13.6%
Other Punctuation 767
 
2.3%
Close Punctuation 394
 
1.2%
Open Punctuation 394
 
1.2%
Dash Punctuation 299
 
0.9%
Math Symbol 43
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 1832
22.5%
G 1599
19.6%
S 690
 
8.5%
M 567
 
7.0%
P 513
 
6.3%
A 384
 
4.7%
D 281
 
3.4%
F 232
 
2.8%
N 220
 
2.7%
R 215
 
2.6%
Other values (16) 1618
19.9%
Lowercase Letter
ValueCountFrequency (%)
a 1505
13.5%
l 1431
12.9%
e 1230
11.1%
i 773
 
7.0%
c 759
 
6.8%
o 739
 
6.6%
r 694
 
6.2%
k 649
 
5.8%
n 438
 
3.9%
t 416
 
3.7%
Other values (16) 2480
22.3%
Decimal Number
ValueCountFrequency (%)
2 1232
16.5%
1 1140
15.3%
0 926
12.4%
6 858
11.5%
4 704
9.4%
8 703
9.4%
5 617
8.3%
3 567
7.6%
9 433
 
5.8%
7 286
 
3.8%
Other Punctuation
ValueCountFrequency (%)
/ 749
97.7%
. 17
 
2.2%
; 1
 
0.1%
Space Separator
ValueCountFrequency (%)
4513
100.0%
Close Punctuation
ValueCountFrequency (%)
) 394
100.0%
Open Punctuation
ValueCountFrequency (%)
( 394
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 299
100.0%
Math Symbol
ValueCountFrequency (%)
+ 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19265
58.1%
Common 13876
41.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 1832
 
9.5%
G 1599
 
8.3%
a 1505
 
7.8%
l 1431
 
7.4%
e 1230
 
6.4%
i 773
 
4.0%
c 759
 
3.9%
o 739
 
3.8%
r 694
 
3.6%
S 690
 
3.6%
Other values (42) 8013
41.6%
Common
ValueCountFrequency (%)
4513
32.5%
2 1232
 
8.9%
1 1140
 
8.2%
0 926
 
6.7%
6 858
 
6.2%
/ 749
 
5.4%
4 704
 
5.1%
8 703
 
5.1%
5 617
 
4.4%
3 567
 
4.1%
Other values (8) 1867
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4513
 
13.6%
B 1832
 
5.5%
G 1599
 
4.8%
a 1505
 
4.5%
l 1431
 
4.3%
2 1232
 
3.7%
e 1230
 
3.7%
1 1140
 
3.4%
0 926
 
2.8%
6 858
 
2.6%
Other values (60) 16875
50.9%

os
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct6
Distinct (%)0.6%
Missing197
Missing (%)16.1%
Memory size9.7 KiB
Android
915 
iOS
103 
OxygenOS
 
3
WindowsPhone
 
3
EMUI
 
2

Length

Max length12
Median length7
Mean length6.6085686
Min length3

Characters and Unicode

Total characters6787
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAndroid
2nd rowAndroid
3rd rowAndroid
4th rowAndroid
5th rowAndroid

Common Values

ValueCountFrequency (%)
Android 915
74.8%
iOS 103
 
8.4%
OxygenOS 3
 
0.2%
WindowsPhone 3
 
0.2%
EMUI 2
 
0.2%
KAIOS 1
 
0.1%
(Missing) 197
 
16.1%

Length

2023-10-22T16:14:12.649975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T16:14:12.778474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
android 915
89.1%
ios 103
 
10.0%
oxygenos 3
 
0.3%
windowsphone 3
 
0.3%
emui 2
 
0.2%
kaios 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
d 1833
27.0%
i 1021
15.0%
n 924
13.6%
o 921
13.6%
A 916
13.5%
r 915
13.5%
O 110
 
1.6%
S 107
 
1.6%
e 6
 
0.1%
y 3
 
< 0.1%
Other values (12) 31
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5638
83.1%
Uppercase Letter 1149
 
16.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1833
32.5%
i 1021
18.1%
n 924
16.4%
o 921
16.3%
r 915
16.2%
e 6
 
0.1%
y 3
 
0.1%
g 3
 
0.1%
x 3
 
0.1%
w 3
 
0.1%
Other values (2) 6
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
A 916
79.7%
O 110
 
9.6%
S 107
 
9.3%
W 3
 
0.3%
P 3
 
0.3%
I 3
 
0.3%
E 2
 
0.2%
M 2
 
0.2%
U 2
 
0.2%
K 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 6787
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1833
27.0%
i 1021
15.0%
n 924
13.6%
o 921
13.6%
A 916
13.5%
r 915
13.5%
O 110
 
1.6%
S 107
 
1.6%
e 6
 
0.1%
y 3
 
< 0.1%
Other values (12) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6787
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1833
27.0%
i 1021
15.0%
n 924
13.6%
o 921
13.6%
A 916
13.5%
r 915
13.5%
O 110
 
1.6%
S 107
 
1.6%
e 6
 
0.1%
y 3
 
< 0.1%
Other values (12) 31
 
0.5%

popularity
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct1224
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean612.5
Minimum1
Maximum1224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-10-22T16:14:12.936109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile62.15
Q1306.75
median612.5
Q3918.25
95-th percentile1162.85
Maximum1224
Range1223
Interquartile range (IQR)611.5

Descriptive statistics

Standard deviation353.48267
Coefficient of variation (CV)0.57711457
Kurtosis-1.2
Mean612.5
Median Absolute Deviation (MAD)306
Skewness0
Sum749700
Variance124950
MonotonicityNot monotonic
2023-10-22T16:14:13.120385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
422 1
 
0.1%
669 1
 
0.1%
717 1
 
0.1%
937 1
 
0.1%
781 1
 
0.1%
1127 1
 
0.1%
945 1
 
0.1%
267 1
 
0.1%
255 1
 
0.1%
963 1
 
0.1%
Other values (1214) 1214
99.2%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1224 1
0.1%
1223 1
0.1%
1222 1
0.1%
1221 1
0.1%
1220 1
0.1%
1219 1
0.1%
1218 1
0.1%
1217 1
0.1%
1216 1
0.1%
1215 1
0.1%

best_price
Real number (ℝ)

HIGH CORRELATION 

Distinct970
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7941.2067
Minimum214
Maximum56082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-10-22T16:14:13.293752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum214
5-th percentile499
Q12599.75
median4728
Q39323
95-th percentile27995.1
Maximum56082
Range55868
Interquartile range (IQR)6723.25

Descriptive statistics

Standard deviation8891.8363
Coefficient of variation (CV)1.1197085
Kurtosis4.7255408
Mean7941.2067
Median Absolute Deviation (MAD)2679
Skewness2.0936991
Sum9720037
Variance79064752
MonotonicityNot monotonic
2023-10-22T16:14:13.474856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2999 8
 
0.7%
2299 5
 
0.4%
4099 5
 
0.4%
252 5
 
0.4%
3490 4
 
0.3%
5299 4
 
0.3%
1699 4
 
0.3%
3590 4
 
0.3%
499 4
 
0.3%
2399 4
 
0.3%
Other values (960) 1177
96.2%
ValueCountFrequency (%)
214 1
 
0.1%
220 1
 
0.1%
235 1
 
0.1%
241 2
 
0.2%
249 4
0.3%
252 5
0.4%
272 2
 
0.2%
273 1
 
0.1%
275 1
 
0.1%
279 1
 
0.1%
ValueCountFrequency (%)
56082 1
0.1%
55338 1
0.1%
51460 1
0.1%
49242 1
0.1%
46503 1
0.1%
46325 1
0.1%
44716 1
0.1%
44232 1
0.1%
42968 1
0.1%
42649 1
0.1%

lowest_price
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct666
Distinct (%)69.1%
Missing260
Missing (%)21.2%
Infinite0
Infinite (%)0.0%
Mean7716.0187
Minimum198
Maximum49999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-10-22T16:14:13.654557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum198
5-th percentile447
Q12399
median4574
Q39262.25
95-th percentile27224.65
Maximum49999
Range49801
Interquartile range (IQR)6863.25

Descriptive statistics

Standard deviation8560.9591
Coefficient of variation (CV)1.1095047
Kurtosis3.805784
Mean7716.0187
Median Absolute Deviation (MAD)2810.5
Skewness1.9415716
Sum7438242
Variance73290020
MonotonicityNot monotonic
2023-10-22T16:14:13.835858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4999 9
 
0.7%
3549 8
 
0.7%
499 8
 
0.7%
3199 6
 
0.5%
699 6
 
0.5%
2199 6
 
0.5%
2999 6
 
0.5%
599 6
 
0.5%
899 5
 
0.4%
3399 5
 
0.4%
Other values (656) 899
73.4%
(Missing) 260
 
21.2%
ValueCountFrequency (%)
198 1
 
0.1%
199 1
 
0.1%
209 1
 
0.1%
218 2
 
0.2%
219 1
 
0.1%
220 5
0.4%
222 1
 
0.1%
227 2
 
0.2%
239 2
 
0.2%
259 1
 
0.1%
ValueCountFrequency (%)
49999 1
0.1%
45799 1
0.1%
45120 1
0.1%
44809 1
0.1%
44199 1
0.1%
43396 1
0.1%
42999 1
0.1%
41996 1
0.1%
39620 1
0.1%
37639 1
0.1%

highest_price
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct648
Distinct (%)67.2%
Missing260
Missing (%)21.2%
Infinite0
Infinite (%)0.0%
Mean9883.4108
Minimum229
Maximum69999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-10-22T16:14:14.003735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum229
5-th percentile561.5
Q12887
median5325.5
Q312673.75
95-th percentile34945.15
Maximum69999
Range69770
Interquartile range (IQR)9786.75

Descriptive statistics

Standard deviation11514.937
Coefficient of variation (CV)1.1650772
Kurtosis4.2056208
Mean9883.4108
Median Absolute Deviation (MAD)3430
Skewness2.026239
Sum9527608
Variance1.3259377 × 108
MonotonicityNot monotonic
2023-10-22T16:14:14.175359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3299 8
 
0.7%
8999 7
 
0.6%
3999 7
 
0.6%
7999 6
 
0.5%
3399 6
 
0.5%
4499 6
 
0.5%
7499 5
 
0.4%
2099 5
 
0.4%
8499 5
 
0.4%
1049 5
 
0.4%
Other values (638) 904
73.9%
(Missing) 260
 
21.2%
ValueCountFrequency (%)
229 2
 
0.2%
259 1
 
0.1%
289 2
 
0.2%
291 2
 
0.2%
299 1
 
0.1%
314 1
 
0.1%
317 1
 
0.1%
329 1
 
0.1%
330 1
 
0.1%
333 5
0.4%
ValueCountFrequency (%)
69999 1
0.1%
64999 1
0.1%
59999 2
0.2%
57999 1
0.1%
56999 1
0.1%
53731 1
0.1%
51999 1
0.1%
50999 1
0.1%
50760 1
0.1%
50670 1
0.1%

sellers_amount
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.743464
Minimum1
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-10-22T16:14:14.346825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q326
95-th percentile55
Maximum125
Range124
Interquartile range (IQR)24

Descriptive statistics

Standard deviation20.597006
Coefficient of variation (CV)1.230152
Kurtosis5.3094121
Mean16.743464
Median Absolute Deviation (MAD)7
Skewness2.075041
Sum20494
Variance424.23667
MonotonicityNot monotonic
2023-10-22T16:14:14.523891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 235
19.2%
2 119
 
9.7%
3 72
 
5.9%
4 57
 
4.7%
5 45
 
3.7%
10 36
 
2.9%
6 35
 
2.9%
8 34
 
2.8%
11 29
 
2.4%
9 26
 
2.1%
Other values (84) 536
43.8%
ValueCountFrequency (%)
1 235
19.2%
2 119
9.7%
3 72
 
5.9%
4 57
 
4.7%
5 45
 
3.7%
6 35
 
2.9%
7 24
 
2.0%
8 34
 
2.8%
9 26
 
2.1%
10 36
 
2.9%
ValueCountFrequency (%)
125 2
0.2%
120 1
0.1%
117 1
0.1%
115 1
0.1%
112 1
0.1%
111 1
0.1%
109 1
0.1%
105 1
0.1%
102 2
0.2%
101 1
0.1%

screen_size
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)6.7%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.3943781
Minimum1.4
Maximum8.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-10-22T16:14:14.696370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile2.2
Q15.1625
median6
Q36.4
95-th percentile6.7
Maximum8.1
Range6.7
Interquartile range (IQR)1.2375

Descriptive statistics

Standard deviation1.4769913
Coefficient of variation (CV)0.27380197
Kurtosis0.65931067
Mean5.3943781
Median Absolute Deviation (MAD)0.5
Skewness-1.3955878
Sum6591.93
Variance2.1815034
MonotonicityNot monotonic
2023-10-22T16:14:14.874799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.5 103
 
8.4%
2.4 81
 
6.6%
6.5 78
 
6.4%
6.3 60
 
4.9%
5 58
 
4.7%
6.1 58
 
4.7%
6.4 58
 
4.7%
5.7 53
 
4.3%
1.77 39
 
3.2%
2.8 39
 
3.2%
Other values (72) 595
48.6%
ValueCountFrequency (%)
1.4 2
 
0.2%
1.44 5
 
0.4%
1.77 39
3.2%
1.8 11
 
0.9%
2 4
 
0.3%
2.2 8
 
0.7%
2.3 1
 
0.1%
2.4 81
6.6%
2.8 39
3.2%
3 1
 
0.1%
ValueCountFrequency (%)
8.1 1
 
0.1%
7.6 1
 
0.1%
7.3 4
 
0.3%
7.12 4
 
0.3%
6.9 8
0.7%
6.88 1
 
0.1%
6.82 2
 
0.2%
6.81 2
 
0.2%
6.8 11
0.9%
6.78 2
 
0.2%

memory_size
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)1.3%
Missing112
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean95.700059
Minimum0.0032
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-10-22T16:14:15.043947image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0032
5-th percentile0.032
Q132
median64
Q3128
95-th percentile256
Maximum1000
Range999.9968
Interquartile range (IQR)96

Descriptive statistics

Standard deviation111.92258
Coefficient of variation (CV)1.1695142
Kurtosis15.218553
Mean95.700059
Median Absolute Deviation (MAD)48
Skewness3.1958657
Sum106418.47
Variance12526.663
MonotonicityNot monotonic
2023-10-22T16:14:15.201647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
64 295
24.1%
128 285
23.3%
32 179
14.6%
16 122
10.0%
256 85
 
6.9%
0.032 62
 
5.1%
512 36
 
2.9%
8 19
 
1.6%
4 8
 
0.7%
0.004 6
 
0.5%
Other values (5) 15
 
1.2%
(Missing) 112
 
9.2%
ValueCountFrequency (%)
0.0032 3
 
0.2%
0.004 6
 
0.5%
0.016 4
 
0.3%
0.032 62
 
5.1%
0.064 4
 
0.3%
0.128 1
 
0.1%
4 8
 
0.7%
8 19
 
1.6%
16 122
10.0%
32 179
14.6%
ValueCountFrequency (%)
1000 3
 
0.2%
512 36
 
2.9%
256 85
 
6.9%
128 285
23.3%
64 295
24.1%
32 179
14.6%
16 122
10.0%
8 19
 
1.6%
4 8
 
0.7%
0.128 1
 
0.1%

battery_size
Real number (ℝ)

HIGH CORRELATION 

Distinct151
Distinct (%)12.4%
Missing10
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean3608.2018
Minimum460
Maximum18800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.7 KiB
2023-10-22T16:14:15.365748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum460
5-th percentile950
Q12900
median3687
Q34400
95-th percentile5580
Maximum18800
Range18340
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation1668.2688
Coefficient of variation (CV)0.46235462
Kurtosis10.686759
Mean3608.2018
Median Absolute Deviation (MAD)772
Skewness1.7042709
Sum4380357
Variance2783120.7
MonotonicityNot monotonic
2023-10-22T16:14:15.575216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4000 158
 
12.9%
5000 109
 
8.9%
3000 87
 
7.1%
4500 54
 
4.4%
800 40
 
3.3%
3500 40
 
3.3%
3400 30
 
2.5%
1000 26
 
2.1%
3300 24
 
2.0%
2000 22
 
1.8%
Other values (141) 624
51.0%
ValueCountFrequency (%)
460 2
 
0.2%
600 17
1.4%
650 1
 
0.1%
800 40
3.3%
950 2
 
0.2%
970 6
 
0.5%
1000 26
2.1%
1020 6
 
0.5%
1050 4
 
0.3%
1150 2
 
0.2%
ValueCountFrequency (%)
18800 1
 
0.1%
15800 1
 
0.1%
13000 1
 
0.1%
11000 4
 
0.3%
10300 1
 
0.1%
10000 10
0.8%
9000 3
 
0.2%
8000 3
 
0.2%
7000 1
 
0.1%
6600 3
 
0.2%
Distinct73
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size9.7 KiB
Minimum2013-01-01 00:00:00
Maximum2021-02-01 00:00:00
2023-10-22T16:14:15.757406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:15.949768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-10-22T16:14:07.934984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:57.739960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:59.120634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:00.537497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:01.717380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:03.193380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:04.471252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:05.676738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:06.798030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:08.060487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:57.910802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:59.247892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:00.665226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:01.888850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:03.330147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:04.599584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:05.804662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:06.926748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:08.205407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:58.113391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:59.367490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:00.822061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:02.044358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:03.473229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:04.727904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:05.929687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:07.053134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:08.336795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:58.277448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:59.482203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:00.949458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:02.170681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:03.608466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:04.859840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:06.049758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:07.165498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:08.499849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:58.428645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:59.609560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:01.077936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:02.290720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:03.738052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:05.010987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:06.185076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:07.331279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:08.679586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:58.594523image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:59.853314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:01.207913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:02.424875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:03.925397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:05.161504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:06.315414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:07.458140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:09.165140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:58.723193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:59.997851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:01.334288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:02.563571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:04.073101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:05.288025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:06.436629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:07.575678image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:09.449497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:58.866387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:00.162098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:01.468893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:02.684289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:04.198885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:05.413838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:06.551770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:07.691960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:09.597505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:13:58.987555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:00.341801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:01.587935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:02.820564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:04.328315image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:05.541352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:06.666135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T16:14:07.805130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-22T16:14:16.081870image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Unnamed: 0popularitybest_pricelowest_pricehighest_pricesellers_amountscreen_sizememory_sizebattery_sizeos
Unnamed: 01.0000.1020.1620.1560.1770.099-0.0290.090-0.0480.449
popularity0.1021.0000.3960.4230.4480.5160.4050.3700.2750.131
best_price0.1620.3961.0000.9960.9960.1070.6640.8560.4340.222
lowest_price0.1560.4230.9961.0000.9890.0670.6690.8530.4170.251
highest_price0.1770.4480.9960.9891.0000.1150.6460.8380.4010.255
sellers_amount0.0990.5160.1070.0670.1151.0000.0880.1940.0520.194
screen_size-0.0290.4050.6640.6690.6460.0881.0000.6500.7230.504
memory_size0.0900.3700.8560.8530.8380.1940.6501.0000.3950.149
battery_size-0.0480.2750.4340.4170.4010.0520.7230.3951.0000.226
os0.4490.1310.2220.2510.2550.1940.5040.1490.2261.000

Missing values

2023-10-22T16:14:09.862072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-22T16:14:10.245468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-22T16:14:10.421687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0brand_namemodel_nameospopularitybest_pricelowest_pricehighest_pricesellers_amountscreen_sizememory_sizebattery_sizerelease_date
00ALCATEL1 1/8GB Bluish Black (5033D-2JALUAA)Android4221690.01529.01819.0365.008.02000.010-2020
11ALCATEL1 5033D 1/16GB Volcano Black (5033D-2LALUAF)Android3231803.01659.02489.0365.0016.02000.09-2020
22ALCATEL1 5033D 1/16GB Volcano Black (5033D-2LALUAF)Android2991803.01659.02489.0365.0016.02000.09-2020
33ALCATEL1 5033D 1/16GB Volcano Black (5033D-2LALUAF)Android2871803.01659.02489.0365.0016.02000.09-2020
44Nokia1.3 1/16GB CharcoalAndroid10471999.0NaNNaN105.7116.03000.04-2020
55Honor10 6/64GB BlackAndroid7110865.010631.011099.025.8064.03400.06-2018
66Honor10 Lite 3/32GB BlueAndroid4243999.0NaNNaN26.2132.03400.012-2018
77Honor10 Lite 4/64GB BlackAndroid1344973.04733.05295.066.2164.03400.01-2019
88Honor10 lite 3/128GB BlueAndroid4775100.04990.05222.036.21128.03400.01-2021
99Honor10 lite 3/64GB BlackAndroid2154948.04646.05372.086.2164.03400.012-2018
Unnamed: 0brand_namemodel_nameospopularitybest_pricelowest_pricehighest_pricesellers_amountscreen_sizememory_sizebattery_sizerelease_date
12141214AppleiPhone XS 256GB Gold (MT9K2)iOS112621463.015281.025617.0685.80256.02568.09-2018
12151215AppleiPhone XS 512GB Silver (MT9M2)iOS80322842.017832.027500.0625.80512.02568.09-2018
12161216AppleiPhone XS 64GB Space Gray (MT9E2)iOS118719790.012505.023928.0535.8064.02568.09-2018
12171217AppleiPhone XS Max 256GB Gold (MT552)iOS112824184.018399.030600.0376.50256.03174.09-2018
12181218AppleiPhone XS Max 512GB Space Gray (MT622)iOS84227190.021150.030200.0476.50512.03174.09-2018
12191219AppleiPhone XS Max 64GB Gold (MT522)iOS110122685.016018.027900.0616.5064.03174.09-2018
12201220AppleiPhone XS Max Dual Sim 64GB Gold (MT732)iOS53024600.021939.033720.0286.5064.03174.09-2018
12211221HUAWEInova 5T 6/128GB Black (51094MEU)Android11748804.07999.09999.0186.26128.03750.011-2019
12221222ZTEnubia Red Magic 5G 8/128GB BlackAndroid75218755.018500.019010.026.65128.04500.010-2020
12231223Sigma mobilex-style 35 ScreenNaN952907.0785.0944.0753.50NaN1750.01-2020